Understanding the Key Role of Atmospheric Processing in Determining the Oxidative Potential of Airborne Engineered Nanoparticles
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Inhalation of airborne engineered nanoparticles (ENPs) is an important pathway for population exposure. While there have been numerous studies of the health impacts of pristine ENPs, the impacts of atmospherically transformed ENPs are largely unknown, despite the certainty that atmospheric processing of ENPs will occur. Here, the oxidative potential (OP) of TiO 2, CeO 2, and SiO 2 nanoparticles which had been coated with atmospheric secondary organic material (SOM) from the OH or O 3 oxidation of α-pinene and toluene was investigated. The results indicated that coating of these ENPs with SOM formed at low photochemical ages reduced the OP of redox-active ENPs (TiO 2 and CeO 2 ) and increased the OP of redox-inert ENP (SiO 2 ). However, at a given SOM coating thickness, the overall OP of the particles increased by up to 93% with an increased level of photooxidation, regardless of ENP type. The OP suppression and enhancement observed here were attributed to a physical hindrance of ENP–antioxidant interactions by the SOM and an enhanced peroxide content in SOM (brought about by an increased level of photooxidation), respectively. These results imply that the health risk associated with airborne ENPs is strongly related to their time history during their residence time in the atmosphere, and thus, accounting for the impacts of atmospheric processing should be considered critical for making accurate risk assessments of airborne ENPs and for formulating efficient policies with respect to the control of emerging nanotechnologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".